2020 Volume 56 Issue 10 Pages 483-494
In biological neural networks of living organisms, various firing patterns of nerve cells have been observed, typical examples of which are burst firings and periodic firings. In this paper we propose a learning method which can realize various firing patterns for recurrent spiking neural networks (RSNNs). We have already proposed learning methods of RSNNs in which the learning problem is formulated such that the number of spikes emitted by a neuron and their firing instants coincide with given desired ones. In this paper, in addition to that, we consider several desired properties of a target RSNN and propose cost functions for realizing them. Since the proposed cost functions are not differentiable with respect to the learning parameters, we propose a learning method based on the particle swarm optimization. Furthermore we apply the proposed method to developing a model for “visual feature extraction” in biological system and demonstrate its effectiveness.
